PubMed

Toxics. 2025 Jan 24;13(2):86. doi: 10.3390/toxics13020086.ABSTRACTThe combined pollution of microplastics (MPs) and arsenic (As) has gradually been recognized as a global environmental problem, which calls for detailed investigation of the synergistic toxic effects of MPs and As on plants and their mechanisms. Therefore, the interaction between polystyrene microplastics (PS-MPs) and arsenate (AsO43-) (in the following text, it is abbreviated as As(V)) and its toxic effects on lettuce were investigated in this study. Firstly, chemisorption was identified as the main mechanism between PS-MPs and As(V) by the analysis of adsorption kinetics, adsorption thermodynamics, and Fourier transform infrared spectroscopy (FTIR). At the same time, the addition of As(V) promoted the penetration of PS-MPs through the continuous endodermal region of the Casparis strip. Furthermore, compared with the CK group, it was found that the co-addition of As(V) exacerbated the lowering effect of PS-MPs on the pH value of the rhizosphere environment and the inhibitory effect on root growth. In the P20V10 group, the pH decreased by 33.0%. Compared to the CK group, P20, P20V1, and P20V10 decreased the chlorophyll content by 68.45% (16 SPAD units), 71.37% (17.73 SPAD units), and 61.74% (15.36 SPAD units) and the root length by 19.31% (4.18 cm), 50.72% (10.98 cm), and 47.90% (10.37 cm) in lettuce. P5V10 and P20V10 increased CAT content by 153.54% (33.22 U·(mgprol)-1) and 182.68% ((38.2 U·(mgprol)-1)), Ca by 31.27% and 37.68%, and Zn by 41.85% and 41.85%, but the presence of As(V) reduced Na by 22.85% (P5V1) and 49.95% (P5V10). The co-exposure significantly affected the physiological and biochemical indicators as well as the nutritional quality of the lettuce. Finally, the metabolomic analysis of the lettuce leaves showed that combined pollution with PS-MPs and As(V) affected the metabolic pathways of the tricarboxylic acid cycle (TCA cycle), sulfur metabolism, and pyruvate metabolism. This study provides data for pollution management measures for co-exposure to PS-MPs and As(V).PMID:39997901 | DOI:10.3390/toxics13020086

Metabolites. 2025 Feb 19;15(2):140. doi: 10.3390/metabo15020140.ABSTRACTBackground/Objectives: High-throughput metabolomics data provide a detailed molecular window into biological processes. We consider the problem of assessing how association of metabolite levels with individual (sample) characteristics, such as sex or treatment, depend on metabolite characteristics such as pathways. Typically, this is done using a two-step process. In the first step, we assess the association of each metabolite with individual characteristics. In the second step, an enrichment analysis is performed by metabolite characteristics. Methods: We combine the two steps using a bilinear model based on the matrix linear model (MLM) framework previously developed for high-throughput genetic screens. Our method can estimate relationships in metabolites sharing known characteristics, whether categorical (such as type of lipid or pathway) or numerical (such as number of double bonds in triglycerides). Results: We demonstrate the flexibility and interoperability of MLMs by applying them to three metabolomic studies. We show that our approach can separate the contribution of the overlapping triglyceride characteristics, such as the number of double bonds and the number of carbon atoms. Conclusion: The matrix linear model offers a flexible, efficient, and interpretable framework for integrating external information and examining complex relationships in metabolomics data. Our method has been implemented in the open-source Julia package, MatrixLM. Data analysis scripts with example data analyses are also available.PMID:39997765 | DOI:10.3390/metabo15020140

Metabolites. 2025 Feb 18;15(2):137. doi: 10.3390/metabo15020137.ABSTRACTBackground/Objectives: Broussonetia papyrifera is a tree-producing allergenic pollen that grows in varied climatic conditions worldwide and causes pollen allergies in susceptible humans. This study aimed to investigate B. papyrifera pollen morphology, pollen metabolome, pollen allergenicity, and climate change's impact on the plant habitat suitability in the future. Methods: Tree pollen was collected in spring from different regions of Pakistan. Pollen samples were subjected to morphological analysis, Fourier transform infrared spectroscopy (FTIR), liquid chromatography-mass spectrometry (LC-MS/MS), and immunoblotting. Results: MaxEnt modeling predicted the tree's future-growth invasion into new regions. Scanning electron microscopy (SEM) and FTIR displayed regional differences in pollen morphology and metabolome correlated to shifts in climatic variables. LC-MS/MS analysis detected four lipids that can potentially stimulate inflammatory responses. Pollen protein immunoblotting studies identified a putative 15 kDa novel allergen and verified previously known 40 kDa, 33 kDa, and 10 kDa allergens. B. papyrifera MaxEnt modeling through ACCESS1.0 and CCSM4 under 2-greenhouse gas emissions scenarios {representative concentration pathway (RCP) 4.5 and 8.5} projected the tree invasion by the years 2050 and 2070. Conclusions: The study findings demonstrate that differences in climatic variables affect B. papyrifera-pollen metabolome and predict the habitat suitability of the tree for invasion in the future. The study results provide a model system for studying other species' pollen morphology, metabolome, future habitat suitability for plant invasion, and associated allergies in response to climate change.PMID:39997762 | DOI:10.3390/metabo15020137

Metabolites. 2025 Feb 16;15(2):134. doi: 10.3390/metabo15020134.ABSTRACTObjectives: This study aimed to investigate the regulatory impact of early swimming intervention on striatal metabolism in Shank3 gene knockout ASD model rats. Methods:Shank3 gene knockout exon 11-21 male 8-day-old SD rats were used as experimental subjects and randomly divided into the following three groups: a Shank3 knockout control group (KC), a wild-type control group (WC) from the same litter, and a Shank3 knockout swimming group (KS). The rats in the exercise group received early swimming intervention for 8 weeks starting at 8 days old. LC-MS metabolism was employed to detect the changes in metabolites in the striatum. Results: There were 17 differential metabolites (14 down-regulated) between the KC and WC groups, 19 differential metabolites (18 up-regulated) between the KS and KC groups, and 22 differential metabolites (18 up-regulated) between the KS and WC groups. Conclusions: The metabolism of striatum in Shank3 knockout ASD model rats is disrupted, involving metabolites related to synaptic morphology, and the Glu and GABAergic synapses are abnormal. Early swimming intervention regulated the striatal metabolome group of the ASD model rats, with differential metabolites primarily related to nerve development, synaptic membrane structure, and synaptic signal transduction.PMID:39997759 | DOI:10.3390/metabo15020134

Metabolites. 2025 Feb 16;15(2):133. doi: 10.3390/metabo15020133.ABSTRACTBackground /Objectives Attention deficit hyperactivity disorder (ADHD) is the most common neurodevelopmental condition, and symptoms persist into adulthood. Its etiology, though recognized as multifactorial, is still under discussion. Metabolomics helps us to identify pathways associated with functional and structural changes that may be related to symptomatology. This study aimed to characterize potentially altered metabolic pathways and associated biochemical reactions in ADHD. Methods: A scoping review of experimental research was conducted using PubMed, Web of Science, and Scopus using PRISMA ScR. Fifty-five studies were eligible for data extraction, of which fifteen met the criteria for inclusion in the review. Subsequently, the identified metabolites were analyzed in the context of the literature to recognize possible discordant pathways in the disorder. Results: Two groups of relevant neuromodulators of ADHD were found: precursors of monoamines and polyunsaturated fatty acids. The literature was reviewed to discover potential implicated pathways and new metabolites of interest. Conclusions: The study of ADHD biomarkers should focus on measuring precursor, intermediate, and final metabolites of polyunsaturated fatty acids and monoamines in panels or through untargeted analysis to improve the understanding of the pathology and individualization of treatments.PMID:39997758 | DOI:10.3390/metabo15020133

Metabolites. 2025 Feb 14;15(2):132. doi: 10.3390/metabo15020132.ABSTRACTBackground/Objectives: Liquid chromatography coupled with mass spectrometry (LC-MS) is a commonly used platform for many metabolomics studies. However, metabolite annotation has been a major bottleneck in these studies in part due to the limited publicly available spectral libraries, which consist of tandem mass spectrometry (MS/MS) data acquired from just a fraction of known compounds. Application of deep learning methods is increasingly reported as an alternative to spectral matching due to their ability to map complex relationships between molecular fingerprints and mass spectrometric measurements. The objectives of this study are to investigate deep learning methods for molecular fingerprint based on MS/MS spectra and to rank putative metabolite IDs according to similarity of their known and predicted molecular fingerprints. Methods: We trained three types of deep learning methods to model the relationships between molecular fingerprints and MS/MS spectra. Prior to training, various data processing steps, including scaling, binning, and filtering, were performed on MS/MS spectra obtained from National Institute of Standards and Technology (NIST), MassBank of North America (MoNA), and Human Metabolome Database (HMDB). Furthermore, selection of the most relevant m/z bins and molecular fingerprints was conducted. The trained deep learning models were evaluated on ranking putative metabolite IDs obtained from a compound database for the challenges in Critical Assessment of Small Molecule Identification (CASMI) 2016, CASMI 2017, and CASMI 2022 benchmark datasets. Results: Feature selection methods effectively reduced redundant molecular and spectral features prior to model training. Deep learning methods trained with the truncated features have shown comparable performances against CSI:FingerID on ranking putative metabolite IDs. Conclusion: The results demonstrate a promising potential of deep learning methods for metabolite annotation.PMID:39997757 | DOI:10.3390/metabo15020132

Metabolites. 2025 Feb 13;15(2):126. doi: 10.3390/metabo15020126.ABSTRACTBackground/Objectives: The hog deer (Axis porcinus) is an endangered species facing significant threats from habitat loss and fragmentation, with only captive populations remaining in China. Expanding breeding programs and restoring wild populations are critical strategies for the species' conservation. Achieving this requires the development of an effective health database and the identification of molecular biomarkers for their physiological traits. Methods: In this study, we present the largest blood metabolomics dataset to date for captive hog deer, comprising 73 healthy individuals. We conducted targeted metabolomics to quantify blood hormone levels and untargeted metabolomics to characterize blood metabolic profiles, aiming to evaluate the associations of sex, age, and weight with metabolic profiles. Results: Our results reveal distinct growth patterns between females and males, with males reaching their body weight plateau at a larger size. We observed significant sex differences (p < 0.05) in blood hormones and metabolic profiles. Females exhibited higher levels of progesterone, hydroxyprogesterone, stress hormones (e.g., cortisol), and proline, while males had higher levels of testosterone, uric acid, phenylalanine, and guanidinosuccinic acid. Notably, body weight emerged as a more important factor than gender in explaining variations in the metabolome, particularly in males. Several blood biomarkers were identified as correlating with age and body weight. Specifically, blood progesterone levels in females were linked to both age and body weight, while in males, uric acid, prolylhydroxyproline, and 3-methylhistidine were associated with these factors. The potential significance of these results for the artificial breeding and conservation of hog deer were discussed. Conclusions: Our study provides a metabolic reference for identifying abnormal individuals and offers potential biomarkers for determining the gender, age, and body weight of hog deer. These findings may have significant implications for the artificial breeding and conservation efforts of the species.PMID:39997752 | DOI:10.3390/metabo15020126

Metabolites. 2025 Feb 13;15(2):123. doi: 10.3390/metabo15020123.ABSTRACTMetabolomics is an omics technology that studies the types, quantities, and changes of endogenous metabolic substances in organisms affected by abiotic and biotic factors.BACKGROUND/OBJECTIVES: Based on metabolomics, small molecule metabolites in biological organisms can be qualitatively and quantitatively analysed. This method analysis directly correlates with biological phenotypes, facilitating the interpretation of life conditions. Wheat (Triticum aestivum L.) is one of the major food crops in the world, and its quality and yield play important roles in safeguarding food security.METHODS: This review elaborated on the significance of metabolomics research techniques and methods in enhancing wheat resilience against biotic and abiotic stresses.RESULTS: Metabolomics plays an important role in identifying the metabolites in wheat that respond to diverse stresses. The integrated examination of metabolomics with other omics disciplines provides new insights and approaches for exploring resistance genes, understanding the genetic basis of wheat metabolism, and revealing the mechanisms involved in stress responses.CONCLUSIONS: Emerging metabolomics research techniques to propose innovative avenues of research is important to enhance wheat resistance.PMID:39997748 | DOI:10.3390/metabo15020123

Metabolites. 2025 Feb 12;15(2):122. doi: 10.3390/metabo15020122.ABSTRACTWe aimed to identify the characteristic functional components of pigeon eggs and the differences among pigeon, chicken, and quail eggs. We analyzed the metabolite profiles of three kinds of eggs using an untargeted metabolomics-based approach to better understand the differences in metabolites among pigeon, chicken, and quail eggs. Then, we quantitatively validated the differences in abundance of partial metabolites through a targeted metabolomics-based approach. A total of 692 metabolites were identified in the three types of eggs. A total of 263 significantly differentially abundant metabolites were found between pigeon eggs and chicken eggs, and 263 significantly differentially abundant metabolites were found between pigeon eggs and quail eggs. The metabolites that were significantly more abundant in pigeon eggs than in other eggs were mainly lipids, lipid-like molecules, nucleosides, nucleotides, and their analogues. We identified the eight metabolites that were significantly greater in abundance in pigeon eggs than in chicken eggs and quail eggs and quantitatively validated the differences in abundance of these metabolites. Our study demonstrates that there are more functional components in pigeon eggs than chicken eggs and quail eggs, especially for the prevention and treatment of various disordered glucose and lipid metabolism-related diseases. The discovery of these differentially abundant metabolites paves the way for further research on the unique nutritional functions of pigeon eggs and the further utilization of pigeon egg products.PMID:39997747 | DOI:10.3390/metabo15020122

Metabolites. 2025 Feb 12;15(2):121. doi: 10.3390/metabo15020121.ABSTRACTBackground/Objectives: Irritable bowel syndrome (IBS) is a complex disorder affecting 10% of the global population, but the underlying mechanisms remain poorly understood. By integrating multifluid metabolomics, we aimed to identify metabolite markers of IBS in a large population-based cohort. Methods: We included individuals from TwinsUK with and without IBS, ascertained using the Rome III criteria, and analysed serum (232 cases, 1707 controls), urine (185 cases, 1341 controls), and stool (186 cases, 1284 controls) metabolites (Metabolon Inc.). Results: After adjusting for covariates, and multiple testing, 44 unique metabolites (25 novel) were associated with IBS, including lipids, amino acids, and xenobiotics. Androsterone sulphate, a sulfated steroid hormone precursor, was associated with lower odds of IBS in both urine (0.69 [95% confidence interval = 0.56-0.85], p = 2.34 × 10-4) and serum (0.75 [0.63-0.90], p = 1.54 × 10-3. Moreover, suberate (C8-DC) was associated with higher odds of IBS in serum (1.36 [1.15-1.61]; p = 1.84 × 10-4) and lower odds of IBS in stool (0.76 [0.63-0.91]; p = 2.30 × 10-3). On the contrary, 32 metabolites appeared to be fluid-specific, including indole, 13-HODE + 9-HODE, pterin, bilirubin (E,Z or Z,Z), and urolithin. The remaining 10 metabolites were associated with IBS in one fluid with suggestive evidence (p < 0.05) in another fluid. Finally, we identified androgenic signalling, dicarboxylates, haemoglobin, and porphyrin metabolism to be significantly over-represented in individuals with IBS compared to controls. Conclusions: Our results highlight the utility of a multi-fluid approach in IBS research, revealing distinct metabolic signatures across biofluids.PMID:39997746 | DOI:10.3390/metabo15020121

Metabolites. 2025 Feb 11;15(2):119. doi: 10.3390/metabo15020119.ABSTRACTBackground/Objectives: Bupleuri Radix is a plant in the Apiaceae family Bupleurum Chinense DC. or Bupleurum scorzonerifolium Willd. root. The dissimilarities in the metabolite profiles of plants directly correlate with the disparities in their clinical efficacy. Methods: Therefore, the wild Bupleurum Chinense DC. (YBC) and wild Bupleurum scorzonerifolium Willd. (YNC) were used as research materials. They were analyzed using the UPLC-MS/MS and the similarities and differences were uncovered based on differential metabolites. Results: Our results proved that the differences in clinical efficacy between YBC and YNC may be attributed to their distinct metabolite profiles, as follows: (1) a total of 12 classes of 2059 metabolites were identified in the roots, with phenolic acids, terpenoids, and flavonoids being the most abundant metabolic products, with 2026 shared components between the two, 2045 in YBC, and 2040 in YNC; (2) a total of 718 differential metabolites were identified, accounting for 35.44% of the shared metabolites. Among them, YBC had 452 metabolites with higher content relative to YNC, representing 62.95%, and 266 components with lower content, representing 37.05%; (3) the KEEG enrichment analysis results show that the differential metabolic pathways are flavone and flavonol biosynthesis, linoleic acid metabolism, arachidonic acid metabolism, sesquiterpenoid and triterpenoid biosynthesis, and linolenic acid metabolism. Conclusions: These new findings will serve as a foundation for further study of the BR biosynthetic pathway and offer insights into the practical use of traditional Chinese medicine in clinical settings.PMID:39997744 | DOI:10.3390/metabo15020119

Metabolites. 2025 Feb 11;15(2):116. doi: 10.3390/metabo15020116.ABSTRACTBACKGROUND: The postmortem diagnosis of fatal hypothermia presents a considerable challenge in forensic medicine. Metabolomics, a powerful tool reflecting comprehensive changes in endogenous metabolites, offers significant potential for exploring disease mechanisms and identifying diagnostic markers.METHODS: In this study, we employed ultra-high-performance liquid chromatography-mass spectrometry (UHPLC-MS) to perform a non-targeted metabolomic analysis of liver, stomach, spleen, and musculus gastrocnemius tissues from mice subjected to fatal hypothermia.RESULT: A substantial number of differential metabolites were identified in each tissue: 1601 in the liver, 420 in the stomach, 732 in the spleen, and 668 in the gastrocnemius muscle. The most significantly altered metabolites were as follows: magnoflorine (liver, upregulated, ranked first in fold-change), gibberellic acid (stomach, downregulated, ranked first in fold-change), nitrofurantoin (spleen, upregulated, ranked first in fold-change), and isoreserpin (gastrocnemius muscle, downregulated, ranked first in fold-change). Glycerophospholipid metabolism exhibited notable enrichment in all tissues (spleen: second, liver: tenth, stomach: eleventh, gastrocnemius muscle: twenty-first), as did tryptophan metabolism (spleen: thirteenth, liver: eighth, stomach: third, gastrocnemius muscle: seventeenth).CONCLUSIONS: Our findings provide insights into the metabolic perturbations associated with fatal hypothermia in different tissues and lay a foundation for the identification of potential tissue biomarkers for forensic diagnosis.PMID:39997741 | DOI:10.3390/metabo15020116

Metabolites. 2025 Feb 10;15(2):111. doi: 10.3390/metabo15020111.ABSTRACTBACKGROUND: The role of the indoor microbiome in sick building syndrome (SBS) is well-recognized, yet prior studies have been limited to single-center analyses, limiting a broader understanding and applicability of their findings.METHODS: We conducted a multicenter indoor microbiome and metabolome investigation for SBS, involving 1139 middle school students across three regions in Malaysia (Johor Bahru, Terengganu, and Penang). Using high-throughput amplicon sequencing and untargeted LC-MS, indoor microbiome and metabolites were characterized from classroom dust samples.RESULTS: The study found that the prevalence of SBS symptoms was high across all three centers (51.0% to 54.6%). Environmental characteristics, including indoor NO2 and CO2 concentrations and total weight of indoor dust, were positively associated with SBS (p < 0.01, linear regression). Curtobacterium in Terengganu was negatively associated with SBS, and Clostridium perfringens in Johor Bahru was positively associated with SBS (p < 0.01, FDR < 0.05). Whereas all identified fungal taxa, including an uncharacterized uc_f_Auriculariaceae_sp., Duportella kuehneroides, and Wallemia mellicola, were positively associated with SBS (p < 0.01, FDR < 0.05) in Johor Bahru and Terengganu. Mediation analysis revealed that the adverse health effects of NO2 on SBS were partially mediated by the increased abundance of uc_f_Auriculariaceae_sp. (p < 0.05, total effect mediated 51.40%). Additionally, potential protective metabolites (S-adenosylmethionine, N-acetylserotonin, sphinganine, 4-hydroxy-2-quinolone, and (2E,4Z,8E)-Colneleic acid) were mainly derived from environmental microorganisms, conferring protective effects against nasal symptoms and tiredness. In contrast, synthetic chemicals were associated with higher SBS symptoms, inducing eye and nasal symptoms.CONCLUSIONS: This study emphasizes both the significance of fostering a balanced indoor microbiome/metabolite and the necessity to reduce exposure to deleterious substances, providing new insights for future targeted intervention strategies.PMID:39997738 | DOI:10.3390/metabo15020111

Metabolites. 2025 Feb 10;15(2):113. doi: 10.3390/metabo15020113.ABSTRACTBackground: As a major food crop, maize is highly susceptible to pathogenic bacteria, which greatly reduces its yield and quality. Metabolomics reveals physiological and biochemical changes in organisms and aids in analyzing metabolic changes caused by various factors. Methods: This study utilized metabolomics to examine maize's metabolic changes after NCLB infestation, aiming to uncover related pathways and potential biomarkers. The metabolite measurements were performed during the maize silking stage. Results: PCA showed an obvious dispersion between the treated and untreated groups. OPLS-DA identified 1274 differential metabolites, with 242 being downregulated (mainly phenolics and esters) and 1032 upregulated (primarily organic acids, amino acids, sugars, and derivatives). KEGG annotation revealed 50 affected metabolic pathways, and the biosynthesis of secondary metab-olites and amino acids was significantly enriched. Conclusions: We hypothesized that metabolic pathways related to sugar metabolism, proline metabolism, and jasmonic acid synthesis are associated with NCLB susceptibility. These findings provide critical insights into the metabolic responses of maize to biotic stress, offering a theoretical basis for future research on plant resistance mechanisms.PMID:39997737 | DOI:10.3390/metabo15020113

Metabolites. 2025 Feb 10;15(2):112. doi: 10.3390/metabo15020112.ABSTRACTOsteoarthritis (OA) and rheumatoid arthritis (RA) are joint diseases that share similar clinical features but have different etiologies, making a differential diagnosis particularly challenging. Background/Objectives: Utilizing advanced machine learning (ML) techniques on metabolomic data, this study aimed to identify key metabolites in synovial fluid (SF) that could aid in distinguishing between OA and RA. Methods: Metabolite data from the MetaboLights database (MTBLS564), analyzed using nuclear magnetic resonance (NMR), were processed using normalization, a principal component analysis (PCA), and a partial least squares discriminant analysis (PLS-DA) to reveal prominent clustering. Results: Decision forests and random forest classifiers, optimized using genetic algorithms (GAs), highlighted a selection of a few metabolites-primarily glutamine, pyruvate, and proline-with significant discriminative power. A Shapley additive explanations (SHAP) analysis confirmed these metabolites to be pivotal predictors, offering a streamlined approach for clinical diagnostics. Conclusions: Our findings suggest that a minimal set of key metabolites can effectively be relied upon to distinguish between OA and RA, supported by an optimized ML model achieving high accuracy. This workflow could streamline diagnostic efficiency and enhance clinical decision-making in rheumatology.PMID:39997736 | DOI:10.3390/metabo15020112

Metabolites. 2025 Feb 8;15(2):110. doi: 10.3390/metabo15020110.ABSTRACTBackground/Objectives: Lipids are an important component of human nutrition. Conventional milk is obtained from animals, and dairy milk is consumed by many people worldwide. Recently, milk consumers have been increasingly shifting towards plant-based milk options. The aim of the study was the qualitative identification of lipid metabolites in animal- and plant-based milk, the identification and comparison of the fatty acids (FAs) of milk, and the qualitative identification of the lipid groups among the milk varieties. Methods: Milk samples were obtained from local grocery stores. Lipids were extracted using a modified Folch method and analyzed using nuclear magnetic resonance (NMR) metabolomics. Gas and liquid chromatography mass spectrometry methods (GC-MS and LC-MS) were used to identify the FAs and lipid groups. Lipid weights were compared and the NMR profiles of the lipids analyzed by multivariate statistical analysis. Principal component analysis was performed for the milk lipids obtained from the animal, and plant milk varieties. Results: Clustering of NMR data showed two main clusters: cow/almond/cashew and goat/soy/coconut. GC-MS analysis of the methylated fatty acids (FAs) showed the presence of 12:0, 14:0, 16:0, 16:1, 17:0, 18:0, 18:1, 18:2, 20:1, and 20:2 in all milk types, while FAs 19:0 and 20:4 were observed only in the dairy milk. LC-MS data showed common masses that may indicate the presence of mono- and diacyl glycerols and several lysophospholipids among the different types of milk. Conclusions: This study shows the advantage of using NMR, GC-MS, and LC-MS to differentiate the lipids among different milk types and compare them on one platform.PMID:39997734 | DOI:10.3390/metabo15020110

Metabolites. 2025 Feb 7;15(2):107. doi: 10.3390/metabo15020107.ABSTRACTBackground/Objectives: The germination process initiates an active process of secondary metabolism, which produces a series of secondary metabolites, including flavonoids. Methods: A metabolomics and transcriptomics analysis was conducted on maize grains germinated at three different stages. Results: A total of 374 metabolites were detected in maize grains. From the raw maize grain to various stages of germination, 3 anthocyanins, 61 flavones, 12 flavonols, 13 flavanones, and 6 isoflavones were identified, respectively. An integrated omics analysis discovered that a total of 16 flavonoid metabolites were mapped to 4 KEGG pathways, which were associated with 40 related genes. This indicates that germination has significant benefits in improving the nutritional function of corn kernels. Conclusions: In summary, the findings of this study provide valuable insights into flavonoid metabolites and related genes, demonstrating the profound impact of germination treatment on the nutritional and functional aspects of maize grains.PMID:39997732 | DOI:10.3390/metabo15020107

Metabolites. 2025 Feb 7;15(2):106. doi: 10.3390/metabo15020106.ABSTRACTBackground: Metabolomics serves as a very useful tool for elucidating disease mechanisms and identifying biomarkers. Establishing reference intervals (RIs) of metabolites in a healthy population is crucial to the application of metabolomics in life sciences and clinics. Methods: We enrolled 615 healthy Chinese adults aged between 21 and 85 years. Their health status was ascertained through clinical examinations, biochemical parameters, and medical history. Targeted metabolomics and lipidomics analyses were applied to quantify 705 metabolites and lipids in the serum, establishing RIs and investigating the effect of sex and age on the metabolome and lipidome. Results: This study is the first large-scale effort in China to establish RIs for metabolites in the apparently healthy population. We found that most of the sex-related metabolites, including amino acids, acyl-carnitines and triacylglycerols, had higher concentrations in males, while the other sex-related lipids showed higher concentrations in females. Most of the age-related metabolites increased with age, including those associated with protein synthesis, nitric oxide synthesis, energy metabolism, and lipid metabolism. Conclusions: This study gives the reference intervals of the healthy Chinese metabolome and lipidome and their relationship with sex and age, which facilitates life sciences and precision medicine, especially for disease research and biomarker discovery.PMID:39997731 | DOI:10.3390/metabo15020106

Metabolites. 2025 Feb 6;15(2):102. doi: 10.3390/metabo15020102.ABSTRACTBackground:Citrus Exocortis Viroid (CEVd) is a non-coding RNA pathogen capable of infecting a wide range of plant species, despite its lack of protein-coding ability. Viroid infections induce significant alterations in various physiological and biochemical processes, particularly impacting plant metabolism. This study shows the metabolic changes upon viroid infection in tomato plants (Solanum lycopersicum var. 'MoneyMaker') exhibiting altered levels of salicylic acid (SA), a key signal molecule involved in the plant defence against this pathogen. Methods: Transgenic RNAi_S5H lines, which have the salicylic acid 5-hydroxylase gene silenced to promote SA accumulation, and NahG lines, which overexpress a salicylate hydroxylase to degrade SA into catechol and prevent its accumulation, were used to establish different SA levels in plants, resulting in varying degrees of resistance to viroid infection. The analysis was performed by using gas chromatography-mass spectrometry (GC-MS) to explore the role of volatile organic compounds (VOCs) in plant immunity against this pathogen. Results: Our results revealed distinct volatile profiles associated with plant immunity, where RNAi_S5H-resistant plants showed significantly enhanced production of monoterpenoids upon viroid infection. Moreover, viroid-susceptible NahG plants emitted a broad range of VOCs, whilst viroid-tolerant RNAi_S5H plants exhibited less variation in VOC emission. Conclusions: This study demonstrates that SA levels significantly influence metabolic responses and immunity in tomato plants infected by CEVd. The identification of differential emitted VOCs upon CEVd infection could allow the development of biomarkers for disease or strategies for disease control.PMID:39997727 | DOI:10.3390/metabo15020102

Metabolites. 2025 Feb 6;15(2):101. doi: 10.3390/metabo15020101.ABSTRACTBackground/Objectives: Determining appropriate cellular objectives is crucial for the system-scale modeling of biological networks for metabolic engineering, cellular reprogramming, and drug discovery applications. The mathematical representation of metabolic objectives can describe how cells manage limited resources to achieve biological goals within mechanistic and environmental constraints. While rapidly proliferating cells like tumors are often assumed to prioritize biomass production, mammalian cell types can exhibit objectives beyond growth, such as supporting tissue functions, developmental processes, and redox homeostasis. Methods: This review addresses the challenge of determining metabolic objectives and trade-offs from multiomics data. Results: Recent advances in single-cell omics, metabolic modeling, and machine/deep learning methods have enabled the inference of cellular objectives at both the transcriptomic and metabolic levels, bridging gene expression patterns with metabolic phenotypes. Conclusions: These in silico models provide insights into how cells adapt to changing environments, drug treatments, and genetic manipulations. We further explore the potential application of incorporating cellular objectives into personalized medicine, drug discovery, tissue engineering, and systems biology.PMID:39997726 | DOI:10.3390/metabo15020101